• Journal of the Korea Society of Computer and Information
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• v.22 no.4
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• pp.17-24
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• 2017

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.17-20
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• 2006

Inside the global management environment that is going through sudden changes, businesses are concentrating on putting in much hard work to establish the most competitive management strategy for its survival. As a executing tool and following the strategy establishment, BSC is being introduced and used by many businesses and public institutions, but instead of being used as a tool with the proper purpose of executing strategies, in reality, it is mainly being used to measure and evaluate outcomes. Of the several reasons why this is so, one important issue and its fundamental cause is that although BSC had been developed as a strategy executing tool, it is diffcult to understand the degree of execution of strategies because the overall measurement of the KPI includes all the strategic and operational parts. Through case study, this article acts on presenting the methodology of measuring the degree of execution of strategies that apply the BSC and PM frameworks.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.147-153
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• 2016

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.125-129
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• 2016

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• Journal of the Korean Society of Systems Engineering
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• v.13 no.1
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• pp.73-78
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• 2017

Space development program is a large and complex system consisting of a multi-disciplinary high-end technologies and it is important to implement a program management system connected with systems engineering as well as to develop critical technologies. Major organizations in space fields carry out effective budget execution management and operation according to the strategy and objective of space development using information systems. Korea Space Launch Vehicle II(KSLV-II) has adopted a cost management plan using a system engineering to complete the program within the assigned schedule and budget. This paper introduces the budget execution management system applied to KSLV-II budget management and the budget execution dashboard system for supporting program decision making.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.723-724
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• 2009

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.45-52
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• 2008

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-Performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.35-57
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• 2021

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.15-20
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• 2021

The imprecise real-time scheduling can be used for minimizing the bad effects of timing faults by leaving less important tasks unfinished if necessary when a transient overload occured. In the imprecise scheduling, every time-critical task can be logically decomposed into two tasks : a mandatory task and an optional task. Recently, some studies in this field showed good schedulability performance and minimum total error by deferring the optional tasks. But the schedulability performance of the studies can be shown only when the execution time of each optional task was less than or equal to the execution time of its corresponding mandatory task. Therefore, in this paper, a new deferring strategy is proposed under the reverse execution time restriction to the previous studies. Nevertheless, the strategy produces comparable or superior schedulability performance to the previous studies and can minimize the total error also.